Systems and methods for manipulation of outcomes for virtual sporting events

ABSTRACT

Provided are systems and processes for manipulating outcomes of virtual sporting events. An example method comprises identifying a set of valid event segments for a given time segment of a virtual sporting event. An outcome category is determined for each of the valid event segments, which indicates an outcome of a play corresponding to the valid event segment. Audience input is obtained over a network from a plurality of client devices associated with a plurality of audience members. The audience input indicates a desired outcome for the given time segment. A valid event segment is then selected as a virtual event segment for the given time segment based on at least the desired outcome. Selection of the virtual event segment may comprise biasing the set of valid event segments to obtain a predetermined proportion of valid event segments with outcome categories corresponding to the desired outcome.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 to U.S.application Ser. No. 16/817,381, filed Mar. 12, 2020, entitled SYSTEMSAND METHODS FOR MANIPULATION OF OUTCOMES FOR VIRTUAL SPORTING EVENTSwhich claims priority under 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 62/817,410, filed Mar. 12, 2019, entitled SYSTEMS ANDMETHODS FOR MANIPULATION OF OUTCOMES FOR VIRTUAL SPORTING EVENTS, thecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to a system and associated methods ofaudio and video content processing. In one example, the presentdisclosure relates to generation of virtual sporting events.

BACKGROUND

Virtual sporting events have been rapidly gaining popularity amongseveral industries. Fantasy sports is a type of entertainment involvingimaginary or virtual teams assembled from real players of a professionalsport, which compete based on the statistical performance of thoseplayers in actual games. This performance is converted into points thatare compiled and totaled according to selected rosters, which may becompiled and calculated using computers tracking actual results of theprofessional sport. Esports (also known as electronic sports, e-sports,or eSports) is a form of competition using video games, most commonlytaking the form of organized, multiplayer video game competitions,particularly between professional players and teams. Such multiplayervideo games often involve competitions of professional sports, such asbasketball, soccer, and baseball, featuring fictional players and realplayers from the corresponding professional sport.

Revenue may be generated from broadcasting, advertising, wage placement,and other fees for managing leagues, scoring, and user accounts. Assuch, it is desirable to provide new forms of virtual sporting eventsbased on actual and fictional sports and athletes.

SUMMARY

Provided are various mechanisms and processes for generating virtualsporting events. A set of valid event segments are identified for agiven time segment of a virtual sporting event. An outcome category isdetermined for each of the valid event segments, which indicates anoutcome of a play corresponding to the valid event segment. Audienceinput is obtained over a network from a plurality of client devicesassociated with a plurality of audience members. The audience inputindicates a desired outcome for the given time segment. A valid eventsegment is then selected as a virtual event segment for the given timesegment based on at least the desired outcome. Selection of the virtualevent segment may comprise biasing the set of valid event segments toobtain a predetermined proportion of valid event segments with outcomecategories corresponding to the desired outcome.

In one aspect, which may include at least a portion of the subjectmatter of any of the preceding and/or following examples and aspects, amethod for manipulating outcomes of virtual sporting events comprisesidentifying one or more event segments as a set of valid event segmentsfor a given time segment of a virtual sporting event. The method furthercomprises determining an outcome category for each valid event segmentof the set of valid event segments. The outcome category for each validevent segment indicates an outcome of a play corresponding to the validevent segment. The method further comprises obtaining audience input,over a network, from a plurality of client devices associated with aplurality of audience members, wherein the audience input indicates adesired outcome for the given time segment. The method further comprisesselecting a valid event segment as a virtual event segment for the giventime segment based on at least the desired outcome and the outcomecategory for each valid event segment.

Selecting a valid event segment as a virtual event segment may comprisebiasing the set of valid event segments to obtain a biased set of validevent segments, which comprise a predetermined proportion of valid eventsegments with outcome categories corresponding to the desired outcome,and selecting the virtual event segment from the biased set of validevent segments.

Each event segment of the one or more event segments may compriseportions of reference content data. The reference content data maycomprise reference video, reference audio, and reference statistics of aplurality of original sporting events. Each event segment of the one ormore event segments may comprise corresponding video data segments fromthe reference video, audio data segments from the reference audio, andstatistical play data from the reference statistics. Each event segmentof the one or more event segments may be classified with one or morecategories based on the portion of reference content data correspondingto the event segments, wherein the one or more categories includes theoutcome category.

The method may further comprise retrieving an event segment as a virtualevent segment for each time segment of the virtual sporting eventincluding retrieving the biased virtual event segment for the given timesegment. The method may further comprise desensitizing each of thevirtual event segments by at least partially removing color and soundfrom each of the virtual event segments. The method may further comprisemapping virtual event data to each of the virtual event segments. Thevirtual event data may comprise one or more selected from the groupconsisting of: virtual event audio and fictional character information.The method may further comprise generating virtual event information forthe virtual sporting event based on the mapped virtual event data and aprogress of the virtual sporting event indicated by the virtual eventsegments.

In certain aspects, identifying the one or more event segments as theset of valid event segments may comprise receiving historical play datacorresponding one or more historical sporting events, and determiningprobabilities of occurrence for different play types at differentprogress points based on the historical play data. Then for the giventime segment, a current progress point may be determined based onvirtual event information for virtual event segments selected for one ormore previous time segments. Then for the given time segment, the one ormore valid event segments may then be identified based on theprobabilities of occurrence such that the set of valid event segmentsincludes a proportion of valid event segments with play typescorresponding to the probabilities of occurrence for different playtypes.

In certain aspects, identifying the one or more event segments as theset of valid event segments may comprise receiving a programmedobjective, wherein the programmed objective includes one or morepredetermined conditions, and determining current virtual eventinformation based on virtual event segments selected for previous timesegments. Then for the given time segment, it is determined if thepredetermined condition is satisfied based on the current virtual eventinformation. If the predetermined condition is satisfied, the one ormore valid event segments are identified based the programmed objectivesuch that each valid event segment includes a category that satisfiesthe programmed objective.

The method may further comprise displaying the desensitized virtualevent segments in conjunction with associated virtual event data andvirtual event information, obtaining narrative audio corresponding tothe desensitized virtual event segments and corresponding virtual eventdata, creating a virtual event audio file including non-interactiveaudio including the narrative audio and corresponding virtual eventaudio, and transmitting the virtual event audio file, via the network,to the plurality of client devices associated with the plurality ofaudience members.

In certain aspects, identifying the one or more event segments as theset of valid event segments comprises, for the given time segment,obtaining audience feedback data from the plurality of client devicesassociated with the plurality of audience members, wherein the audiencefeedback data indicates a desired category, and identifying one or morevalid event segments that include a category corresponding to thedesired category.

In another aspect, a method for manipulating outcomes of virtualsporting events comprises generating a set of complete virtual sportingevents, each complete sporting event comprising a possible combinationof event segments, each event segment corresponding to a time segment ofthe respective complete virtual sporting event. The method furthercomprises determining an outcome of each complete virtual sporting eventin the set of complete virtual sporting events, and obtaining audienceinput, over a network, from a plurality of client devices associatedwith a plurality of audience members, wherein the audience inputindicates a desired outcome. The method further comprises selecting acomplete virtual sporting event as a chosen virtual sporting event basedon at least the desired outcome and the outcome of each complete virtualsporting event.

Selecting a complete virtual sporting event the chosen virtual sportingevent may comprise biasing the set of complete virtual sporting eventsto obtain a biased set of complete virtual sporting events. The biasedset may comprise a predetermined proportion of complete virtual sportingevents with outcomes corresponding to the desired outcome. The chosenvirtual sporting event may then be selected from the biased set ofcomplete virtual sporting events.

The method may further comprise retrieving the event segmentscorresponding to the chosen virtual sporting event as virtual eventsegments, and desensitizing each of the virtual event segments by atleast partially removing color and sound from each of the virtual eventsegments. The method may further comprise mapping virtual event data toeach of the virtual event segments. The virtual event data comprises oneor more selected from the group consisting of: virtual event audio andfictional character information. The method may further comprisegenerating virtual event information for the chosen virtual sportingevent based on the mapped virtual event data and a progress of thechosen virtual sporting event indicated by the virtual event segments.

Other implementations of this disclosure include corresponding devices,systems, and computer programs, and associated methods for generating avirtual sporting event. These other implementations may each optionallyinclude one or more of the following features. For instance, anon-transitory computer readable medium stores one or more programsconfigured for execution by a computer. The one or more programscomprise instructions for implementing the described method. Alsoprovided is a system comprising one or more processors, memory, and oneor more programs comprising instructions for implementing the describedmethod.

These and other examples are described further below with reference tothe figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example network architecture for implementing varioussystems and methods of the present disclosure, in accordance with one ormore embodiments.

FIG. 2 is an example virtual event system for generating virtualsporting events, in accordance with one or more embodiments.

FIG. 3 illustrates a process flow for segmentation of original contentby segmentation module, in accordance with one or more embodiments.

FIG. 4 is an example method for virtual event creation, in accordancewith one or more embodiments.

FIG. 5 is an example user interface for displaying a video component ofa virtual sporting event, in accordance with one or more embodiments.

FIG. 6A illustrates an example method for selecting event segments usinga Virtual Coach or Virtual Team method, in accordance with one or moreembodiments.

FIG. 6B illustrates an example Principled Play method for selection ofevent segments, in accordance with one or more embodiments.

FIG. 6C illustrates an example Interactive Play method for selection ofevent segments, in accordance with one or more embodiments.

FIG. 6D illustrates an example Exhaustive Play method for selection ofevent segments, in accordance with one or more embodiments.

FIG. 7A illustrates an example process for manipulating selection ofevent segments based on audience input, in accordance with one or moreembodiments.

FIG. 7B illustrates a process flowchart for manipulating event segmentselection, in accordance with one or more embodiments.

FIG. 8A illustrates an example method for manipulating the outcome of avirtual sporting event based on audience input, in accordance with oneor more embodiments.

FIG. 8B illustrates a process flowchart for manipulating virtualsporting event outcomes, in accordance with one or more embodiments.

FIG. 9 illustrates a particular example of a computer system that can beused with various embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the presented concepts. Thepresented concepts may be practiced without some or all of thesespecific details. In other instances, well known process operations havenot been described in detail so as to not unnecessarily obscure thedescribed concepts. While some concepts will be described in conjunctionwith the specific examples, it will be understood that these examplesare not intended to be limiting. On the contrary, it is intended tocover alternatives, modifications, and equivalents as may be includedwithin the spirit and scope of the present disclosure as defined by theappended claims.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure.Particular embodiments of the present disclosure may be implementedwithout some or all of these specific details. In other instances, wellknown process operations have not been described in detail in order notto unnecessarily obscure the present disclosure.

For example, the techniques of the present invention may be described inthe context of various sporting events, such as football games, baseballgames, basketball games, etc. However, it should be noted that thetechniques of the present invention may also be applied to audio orvideo content for various other types of virtual events, such fictionalstories or literary events, computer gaming or electronic sports, etc.The techniques of the present invention may be described in the contextof particular protocols, such as Wi-Fi or Bluetooth. However, it shouldbe noted that the techniques of the present invention may also beapplied to variations of protocols. In the following description,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. Particular example embodimentsof the present invention may be implemented without some or all of thesespecific details. In other instances, well known process operations havenot been described in detail in order not to unnecessarily obscure thepresent invention.

Various techniques and mechanisms of the present invention willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present inventionunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present invention will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

The systems and methods described herein can be implemented tomanipulate outcomes of virtual sporting events. Reference content datacorresponding to original sporting events may be segmented into aplurality of event segments based on predetermined milestones. Eachevent segment may be classified with one or more outcome categories. Avirtual sporting event may comprise one or more consecutive timesegments. One or more event segments are selected as a virtual eventsegment for each time segment based on a combination of various segmentselection methods. In some embodiments, event segments may be selectedat random.

However, in some embodiments, a virtual coach or virtual team method mayselect event segments based on estimated probabilities of occurrence ofdifferent plays based on historical event data corresponding toparticular teams or coaches. In some embodiments, a principled playselection method may select event segments based on various programmedobjectives. In some embodiments, an interactive play selection methodmay select event segments based on user input received from variousclient devices.

In some embodiments, game manipulation techniques may be implemented onthe virtual sporting event. Various audience input may be received toinfluence various outcomes by influencing the probabilities of whichevent segments are selected as virtual event segments for various timesegments. As such, the outcome of an original sporting event may bebiased by audience interaction.

The abovementioned methods may identify a set of valid event segmentsfor a given time segment. The set of valid event segments may then bebiased toward a particular outcome based on user input received fromlisteners. Such input may be various forms of audience participation,including audio input (cheers, jeers, yelling), wagers, listenerlocation, listener profile information, etc. For example, if a majorityof listeners indicate that they support a particular team, then the userinput may indicate that a successful play for the particular team is thedesired outcome for the given time segment.

The set of valid event segments may be biased to include a particularproportion of valid event segments with outcome categories thatcorrespond to the desired outcome. For example, the set of valid eventsegments may be biased to include only valid event segmentscorresponding to the desired outcome. As another example, the set may berefined to include 80% of event segments corresponding to the desiredoutcome. A valid event segment is then be selected from the biased setas a virtual event segment for the given time segment.

Example Embodiments

With reference to FIG. 1 , shown is an example network architecture 100for implementing various systems and methods of the present disclosure,in accordance with one or more embodiments. The network architecture 100includes a number of client devices 102-108 communicably connected toone or more server systems 112 and 114 by a network 110.

In some embodiments, server systems 112 and 114 include one or moreprocessors and memory. The processors of server systems 112 and 114execute computer instructions (e.g., network computer program code)stored in the memory to perform functions of a network data exchangeserver. In various embodiments, the functions of the network dataexchange server may include generating virtual sporting events withcorresponding audio and video content and/or transmitting thecorresponding audio and video content to one or more client devices.

In some embodiments, server system 112 is a content server configured toreceive, process, and store content and information. In some embodimentsserver system 114 is a dispatch server configured to transmit and/orroute network data packets including network messages. In someembodiments, content server 112 and dispatch server 114 are configuredas a single server system that is configured to perform the operationsof both servers. In some embodiments, various functions of serversystems 112 and 114 may be divided among additional other server orsub-server systems.

In some embodiments, the network architecture 100 may further include adatabase 116 communicably connected to client devices 102-108 and serversystems 112 and 114 via network 110. In some embodiments, network data,or other information such as user information, user input, and deviceinformation may be stored in and/or retrieved from database 116. In someembodiments, the server systems described herein may include databases,such as database 116, as integrated components to function as memory forstorage.

Users of the client devices 102-108 access the server system 112 toparticipate in a network data exchange service. For example, the clientdevices 102-108 can execute web browser applications that can be used toaccess the network data exchange service. In another example, the clientdevices 102-108 can execute software applications that are specific tothe network (e.g., networking data exchange “apps” running onsmartphones).

Users interacting with the client devices 102-108 can participate in thenetwork data exchange service provided by the server system 112 bydistributing digital content, such as text comments (e.g., updates,announcements, replies), audio files, digital photos, videos, onlineorders, payment information, activity updates, location information, orother appropriate electronic information. In some implementations,information can be posted on a user's behalf by systems and/or servicesexternal to the network or the server system 112. For example, the usermay transmit audio or video content to a program broadcasting website,and with proper permissions that website may cross-post the audio orvideo content to the network on the user's behalf. In another example, asoftware application executed on a mobile device, with properpermissions, may use global positioning system (GPS) capabilities todetermine the user's location and automatically update the network withhis location (e.g., “At Home”, “At Work”, “In San Francisco, Cal.”).

In some implementations, the client devices 102-108 can be computingdevices such as laptop or desktop computers, smartphones, personaldigital assistants, portable media players, tablet computers, or otherappropriate computing devices that can be used to communicate with anelectronic social network. In some implementations, the server system112 can include one or more computing devices such as a computer server.In various embodiments, a client device may correspond to a user such asan administrator or developer that uses the client device for generationof a virtual sporting event.

In some implementations, the server system 112 can represent more thanone computing device working together to perform the actions of a servercomputer (e.g., cloud computing). In some implementations, the network110 can be a public communication network (e.g., the Internet, cellulardata network, dial up modems over a telephone network) or a privatecommunications network (e.g., private LAN, leased lines).

Virtual Event Systems

With reference to FIG. 2 , shown is an example virtual event system 200for generating virtual sporting events, in accordance with one or moreembodiments. In various embodiments, virtual event system 200 may be acombination of one or more of server systems 112 and 114, and database116, described within system architecture 100. In various embodiments,virtual event system 200 comprises original content database 210,associated content database 212, segmentation module 220, segmentdatabase 230, segment selection module 240, event data module 250, eventdatabase 252, audio creation module 260, audio content integrationmodule 262, delivery module 270, and interactive audio module 280.

Each module within virtual event system 200 may be implemented as aseparate server system or configured as a single server system invirtual event system 200 that is configured to perform the operations ofall modules. Similarly, each database within virtual event system 200may be implemented as a separate database or configured as a singledatabase system that is configured to perform the operations of alldatabases. One or more methods described herein may be discussed withreference to FIG. 2 .

In various embodiments, original content database 210 stores originalcontent including audio and video content data associated with one ormore original events, such as original sporting events (Americanfootball, soccer, baseball, basketball, hockey, tennis, golf, etc.). Theoriginal content data may be content associated with live or recordedcoverage of an original sporting event that is broadcast overtelevision, radio, or other broadcasting media, such as the internet.

The original content may correspond with associated content provided orstored in associated content database 212 based on various timepositions or other chronological measurements or markers. For example,the original content data may comprise a true time corresponding to theactual real world time the original content was recorded or broadcast.The original content may also comprise a run time corresponding to thelength of the video or audio of the original content data file. Theoriginal content may additionally, or alternatively, comprise a clocktime corresponding to an in-game clock or timer for the recorded event.The associated content may also be associated with a correspondingactual time, clock time, or run time. Some examples of the associatedcontent are various other footage of the sporting event, sounds from thefield, player sounds, crowd or spectator sounds, sounds from gameofficials or referees, commentary from a sports announcer orsportscaster, etc. In some examples, the associated content is createdbased on original content that has been extracted or separatelyrecorded. Associated content may further include statistical play datacorresponding to the original sporting event. For example, each play ordown of a football game stored in original content database 210 mayinclude statistical data for each player involved in such play,including player information, running yards, passing yards, total yardsgained or lost, play type, tackles, down information, etc. In someembodiments, these statistics may also be derived from audio and videoof the original content.

In some embodiments, associated content database 212 is separate fromoriginal content database 210. However, in some embodiments, originalcontent database 210 and associated content database 212 are configuredas a single database system that is configured to perform the operationsof both databases. The original content and the associated content maybe stored in any one of various types of video file formats, such asAudio Video Interleave (AVI), Flash Video Format (FLV), Windows MediaVideo (WMV), Apple QuickTime Movie (MOV), and Moving Pictures ExpertGroup 4 (MP4). However, it should be recognized that the original andassociated content may comprise various other file formats.

Segmentation of Original Content Data

In various embodiments, segmentation module 220 is configured toseparate the original content and associated content, into eventsegments based on predetermined milestones (e.g., down in a footballgame, at bat in a baseball game, clock time in a basketball game, eachpitch thrown in a baseball game). Various techniques may be implementedto identify the predetermined milestones, such as optical characterrecognition (OCR) of information shown within the video of the originalcontent, statistical play data, motion detection, audio recognition ofvarious audio and sounds in the original content. For example,segmentation module 220 may be configured to detect the clock time,closed caption text, changes in downs or yardage, as well as penaltiesand scores graphically displayed in the video using OCR techniques andmark the particular time position of the original content forsegmentation. As another example, segmentation module 220 is configuredto recognize various audio and sounds in the original content, such asby identifying a whistle sound from a referee, which may signal the endof each down, penalty, or other event, in a football game. Thus,segmentation module 220 may be configured to detect particular soundsand audio, and mark the original content for segmentation at each timeposition a whistle sound is detected.

In some embodiments, milestones may be determined based on statisticalplay data. For example, statistical play data may record the timeposition (actual time, run time, or clock time) of each down in afootball game. In some embodiments, motion detection may be implementedto identify the predetermined milestones of a sporting event. Forexample, the start or end of each down in the football game may beidentified based on the motion of players on the field in the video.

The original content is separated into event segments based on thepredetermined milestones. With reference to FIG. 3 , shown is a flowprocess 390 for segmentation of original content 320 by segmentationmodule 220, in accordance with one or more embodiments. As an example,original content 320 may be a recording of a televised football game andcomprise audio data and video data. As previously described, originalcontent 320 may include associated content 322 comprising supplementalaudio and video data, as well as statistical play data.

In various embodiments, segmentation module 220 separates originalcontent 320 into one or more event segments 330 by marking the beginningand end of each event segment 330 in the original content based on themilestones identified at operation 304. The time position in theoriginal content at which each identified milestone occurs may berecorded as a mark-in point corresponding to the beginning of an eventsegment. Additionally, or alternatively, the time position at which amilestone occurs may be recorded as a mark-out point corresponding tothe end of an event segment. In various embodiments, the mark-in pointor the mark-out point may correspond to the particular actual time,clock time, or run time of the original content. As such, segmentationinformation, including the mark-in point and/or mark-out point of eachevent segment, is stored in segment database 230. In some embodiments,segment database 230 may be a table level distributed database system.In some embodiments, the mark-in point and/or mark-out point of eachevent segment is stored as metadata associated with the original contentdata. Thus, each of the event segments 330 may comprise a segmentedvideo component and a segmented audio component defined by a mark-inpoint and/or mark-out point. As illustrated, exemplary event segment330-A of event segments 330 comprises video segment 332-A and audiosegment 334-A.

Other associated content corresponding to the original content may alsobe segmented at segmentation module 220. In some embodiments, segmentdatabase 230 may further store segmentation information corresponding tothis associated content. In certain embodiments, statistical play datamay also be marked and associated with corresponding event segments 330based on timestamps for each recorded statistic. In some embodiments,social media data may also be marked and associated with correspondingevent segments 330. In some embodiments, social media data may bereceived from various sources, servers, or databases and stored asassociated content in associated content database 212. This social mediadata may be received by the segmentation module and correlated with thetiming of the broadcast of the original sporting event of the originalcontent. The timestamp for a statistic or social media post may then besynched to the original content based on actual time, clock time in theoriginal sporting event, or a particular run time position of theoriginal content data file. As such, the particular statistical playdata or social media data may be associated with a particular eventsegment in the segment database, such as statistical play data 336-A ofevent segment 330-A or social media data 338-A of event segment 330-A,respectively.

In various embodiments, original content data and associated contentdata may be processed multiple times by different techniques to refinethe identification of the mark-in or mark-out points of the eventsegments. In the example of a football game, OCR techniques may be firstused to identify the number of the down for each play indicated by agraphical information panel in the original content. Next, motiondetection techniques may then be used to further pinpoint when each playstarts or ends. Although OCR techniques may be accurate in reading thetext of the graphical information panel, the indication of the downnumber may not accurately correspond with the exact start and end of theplay corresponding to the indicated down. However, OCR identificationmay utilize relatively less processing power than other segmentidentification techniques, and may be first used to process the entirelength of the original content of a game to identify the approximatemark-in point of each event segment in the original content. In someembodiments, motion detection techniques may require relatively moreprocessing power, but may be more accurate in determining the start of aplay. As such, the motion detection may be implemented only on portionsof the original content, such as during the 5 seconds before and 5seconds after the mark-in point corresponding to each change in downnumber previously determined by OCR. The segmentation module may thenadjust or update the mark-in or mark-out points of the event segmentsaccordingly in the segmentation database. Thus, event segments may bemore accurately identified using less overall processing power and time.It should be recognized that various video and audio processingtechniques may be more suited to identify event segments for differentsporting events.

Classifying Event Segments

In various embodiments, event segments may be classified into one ormore categories based on identified characteristics in the eventsegments. Various characteristics of each event segment be identifiedand associated with the event segment. In various embodiments, thecharacteristics are identified based on the audio data, video data,and/or associated content associated with the event segment. Theidentified characteristics may be stored as segmentation information inthe segment database. The various characteristics may be based onstatistics extrapolated from statistical play data corresponding to theevent segment. Statistical play data may indicate various statisticalplay characteristics including play type, yardage gained or lost,resulting penalties, position of players, etc.

The various characteristics may also include audio characteristics andvideo characteristics. Audio characteristics determined from audio datain the audio segment may include sounds related to players, officials,the crowd or spectators, or other sounds from the game, as well as thevolume of the sounds. The various video characteristics may bedetermined based on video and image data from the video segment. Videocharacteristics may include camera speed, player movement speed, playtype, etc.

In various embodiments, the audio and video processing techniquespreviously described may also be implemented to identify thecharacteristics of the event segments. OCR techniques may be used toanalyze closed captioning text or other onscreen text to determinewhether the event segment involves a penalty, score, or turnover. Neuralnetworks and machine learning techniques may also be implemented torecognize play types, such as passes or runs in a football game.Segmentation module 220 may also include a neural network or machinelearning techniques trained to recognize types of crowd sounds, such ascheering or booing.

Other characteristics may include viewer characteristics based on thesocial media data that has been associated with a particular eventsegment. Viewer characteristics may include types and amount of feedbackfrom viewers of the original sporting event other than the crowd orspectators present at the original sporting event. As used herein,people present at the original sporting event may be referred to as“spectators” or “crowd”, while the term “viewer” refers to a personwatching or listening to the original sporting event that are notspectators or crowd members, such as over television or internetbroadcast for example. As used herein, the term “audience” refers to apeople viewing or listening to a virtual sporting event generated by thedescribed systems and methods. Viewer characteristics may be determinedbased on the social media data associated with the event segment. OCRtechniques and word recognition may also be implemented to determine andcharacterize whether the social media posts were positive or negativetoward the corresponding play or event. Artificial neural networks andother machine learning methods may be implemented for such naturallanguage text reasoning.

Once characteristics are identified, the event segments may beclassified with categories based on the identified characteristics.Various categories may be used to classify the event segments, and eventsegments may be classified into multiple categories. In someembodiments, an event segment may be classified by associating one ormore tags with the event segment, where each tag corresponds to acategory, such as tags 350-A of exemplary event segment 330-A. Suchclassifications may then be stored in the segment database. In someembodiments, the classifications are stored in metadata of the originalcontent data or associated content data.

In various embodiments, categories may include statistical playcategories based on statistical play characteristics, such as playersinvolved, length of the play, timing of the play in the game (timeposition), type of play, result of the play, position on the field, etc.For example, statistical play categories for event segments of afootball game may include run plays, pass plays, special plays, punts,etc. Event segments categorized as pass plays may further be classifiedinto categories of plays involving the running back, the tight end,turnover passes, etc.

Outcome categories may also be determined from statistical play data toindicate the success or failure of a play corresponding to the eventsegment. For example, event segments categorized as pass plays mayfurther be classified based on position of players involved, or resultsuch as incomplete passes and complete passes. As another example, eventsegments categorized a run plays may further be classified into positiveor negative yardage categories. The categories may also indicate thedegree of success or failure. For example, event segments classified mayfurther be classified by amount of yardage gained or lost. Categoriespertaining to yardage gained or lost may be based on ranges of yardsgained or lost. In some embodiments, the range of yardage may vary basedon frequency of such events. For example, plays gaining between 0-15yards may be divided into separate categories corresponding to eachnumber of yards gained because they occur more often. Plays gainingbetween 15-19 yards may be grouped into one single category, and playsgaining more yardage may be grouped into a category with a larger range,such as 20-29 yards gained, 30-49 yards gained, and 50+ yards gained, asplays resulting in such yardage occur with less frequency.

In general, categories based on statistical characteristics maycorrespond to objective qualities of an event segment. However,statistical characteristics may also be used to determine subjectivequalities, such as play difficulty. Other categories based on identifiedaudio and video characteristics may correspond to subjective qualities,but may be classified using objective measures, as described above. Theother categories may include play intensity, play difficulty, or playspeed. For example, event segments may be categorized by high, medium,and low play intensity; high, medium, and low play difficulty; and fastand slow play speed. Event segments may be placed into such categoriesbased on audio or video characteristics as described above. Categoriesfor play intensity, play difficulty, and play speed of an event segmentmay be classified based on video data from the corresponding videosegment and/or audio data from the corresponding audio segment.

Other categories may include crowd sentiment. For example, eventsegments may be categorized as positive, negative, or neutral crowdsentiment, as well as high, medium, or low sentiment. The crowdsentiment of an event segment may be classified based on audiocharacteristics of crowd sounds, including types of crowd sounds andvolume of the crowd sounds. Event segments may also be categorized bycrowd sentiment based on the characteristics of corresponding socialmedia data.

Virtual Event Creation

The stored and classified event segments may then be concatenatedtogether in various orders to form a complete virtual sporting event, aswill be further described. Segment selection module 240 may beconfigured to select one or more event segments for each time segment ofthe virtual sporting event. In some embodiments, the virtual sportingevent is comprised of one or more consecutive time segments. Each timesegment may vary in length of time, and may comprise one or more eventsegments. In some embodiments, the length of each time segment may beequal to the length of the one or more event segments selected for suchtime segment. With reference to FIG. 4 , shown is an example method 400for virtual event creation, in accordance with one or more embodiments.Method 400 may be implemented by a server of virtual event system, suchas segment selection module 240 and/or audio creation module 260.

At operation 402, a set of valid event segments for a particular timesegment of the virtual event is identified based on event-specificconstraints. In some embodiments, an event segment may be selected for aparticular time segment subject to certain constraints based on theevent segment selected for a previous time segment. The constraints maybe determined by virtual game statistics or virtual statistical playdata derived from virtual event data mapped to one or more previous timesegments, as further described below. Such constraints may correspond tothe progression of the game, such as field position of the ball in afootball game or the number of outs in an inning in a baseball game. Forexample, in a virtual football game, if the event segment for a timesegment indicates that the ball is at the forty-five yard line, then theevent segment selected for the subsequent time segment may be requiredto start with the ball at, or near, the forty-five yard line. However,selection of event segments for other types of sporting events may notbe subject to such constraints.

Other constraints may be based on remaining clock time in the virtualsporting event. For example, if the virtual event is nearing adesignated stopping point, such as the half time of a basketball game,the valid event segments may include event segments with a length oftime under a predetermined value. Other constraints may require theevent segment to include a particular amount of crowd sentiment orexcitement so that there is minimized change in crowd sentiment levelsbetween event segments of adjacent time positions. In some embodiments,the constraints may be determined by various segment selectionalgorithms further described herein.

Multiple valid event segments may be identified that meet theconstraints for the particular time position of the virtual event. Oneor more valid event segments may then be selected for the time segmentbased on a combination of one or more different segment selectionalgorithms at operation 404. For example, a valid event segment may berandomly selected for the time position. However, other segmentselection algorithms may be implemented. Described herein with referenceto FIGS. 6A-6D are various segment selection algorithms and methodsincluding a Virtual Coach algorithm, a Virtual Team algorithm, aPrincipled Play algorithm, an Interactive Play algorithm, and anExhaustive Play algorithm. One or more of the segment selectionalgorithms may utilize machine learning techniques or neural networks todetermine probabilities of occurrence for various types of plays atvarious field positions and other progress points of a sporting event.

In some embodiments, biasing module 290 may be implemented to manipulatethe outcome of one or more plays, or of the virtual sporting event,based on external stimulus received from one or more client devicescorresponding to audience members of the virtual sporting event.Although biasing module 290 is depicted as a separate module fromsegment selection module 240, in particular embodiments, biasing module290 and segment selection module 240 may be configured as a singlemodule that is configured to perform the operations of both databases.Such external stimulus may comprise various information received fromclient devices including audience participation data or audiencefeedback data. Such external stimulus may indicate a preferred outcomecorresponding to the success of a play in an event segment. In someembodiments, the preferred outcome may correspond to any otheroccurrence in an event segment, such as the occurrence of a penalty, aparticular type of play, etc. As such, the external stimulus may affectthe selection of an event segment for a given time segment. This mayoccur by biasing the available event segments for selection such thatevent segment corresponding to a particular desired outcome are presentat a desired or predetermined proportion or ratio. Methods formanipulating or biasing the selection of event segments or the outcomeof a virtual sporting event are further described with reference toFIGS. 7A and 7B.

Once event segments have been selected for each time segment of thevirtual sporting event, virtual event data is mapped to the selectedevent segments at operation 406. Such selected event segments may betransmitted to event data module 250. For example, segmentationinformation corresponding to the selected event segments may betransmitted to event data module 250, including identification of theselected event segments and identified characteristics and classifiedcategories for the selected event segments. In various embodiments,event data module 250 is configured to map virtual event data to theselected event segments. Virtual event data may be retrieved from eventdatabase 252, and may include virtual audio and fictional characterinformation.

Event database 252 may store various virtual audio corresponding tovarious types of sporting events. Such virtual audio may be artificiallycreated or recorded from other original sporting events. However, insome embodiments, the virtual audio is unrelated to the plays or eventsincluded in the event segments to which they are mapped. For example,virtual audio for a football game may include an audible or dialoguebetween players, contact or hitting sounds, whistles, etc. Event datamodule 250 may select virtual audio for a particular event segment basedon the characteristics determined from the original audio in theoriginal content or associated content.

Event database 252 may also store fictional character informationincluding information about fictional players, coaches, teams,officials. For example, fictional character information may includebiographies and historical statistics. In some embodiments, fictionalcharacter information may be based in whole, or in part, on existing oractual historical individuals, such as players, coaches, and teams ofthat sport. For example, two fictional teams may be selected for avirtual football game. The players on the fictional teams may then bemapped to the players included in the event segments selected for thevirtual football game based on the respective positions.

In some embodiment event data module 250 may further aggregate thevirtual event data to derive virtual game statistics or virtualstatistical play data. For example, the cumulative clock time of thevirtual sporting event may be calculated. In some embodiments, the eventdata module 250 may take into account various situations in a sportingevent such as timeouts, pauses, or breaks. As another example, scoringplays may be tallied at each time segment to determine the score at anygiven time position of the virtual sporting event. As another example,penalties may be determined and tracked such that penalty consequencesfor penalty violations for particular sports may be implemented in thevirtual sporting event. For example, if a particular fictional player isejected based on a penalty violation, that fictional player will bereplaced by another fictional player from fictional characterinformation. In some embodiments, such virtual game statistics may betransmitted to segment selection module 240 to affect the selection ofevent segments at operation 402 or operation 404. For example, if aplayer in a hockey match is placed in a penalty box, the segmentselection module may only select event segments where a particular teamplays shorthanded.

In some embodiments, the virtual event data is mapped to correspondingselected event segments at event data module 250, and then transmittedto audio creation module 260 to be displayed with the appropriateselected event segments. At operation 408, a video component of thevirtual event is generated from the selected event segments at audiocreation module 260. Audio creation module 260 may be configured tointegrate the selected event segments and mapped virtual event data intoa video component of the virtual sporting event. In various embodiments,the segment selection information may be transmitted to audio creationmodule 260 which may retrieve the selected segments from the originalcontent stored in original content database 210. As such, the videosegments of the identified segment may be transmitted to the audiocreation module. Audio creation module 260 may be configured to extractonly the identified video segments corresponding to the selected eventsegments. However, in some embodiments, the audio segments of theidentified event segments may also be received at audio creation module260, such as in file formats where audio and video data are combined.The video data of the selected event segments may be positionedchronologically according to the time sequence of their respective timesegments.

In some embodiments, the video component of the virtual sporting eventmay be desensitized by removing color, sound, and other attributes fromthe original content data. In various embodiments, the color may bestripped from the video via signal processing techniques. In variousembodiments, the audio data may be isolated and removed from the eventsegment data. Other attributes of the video which may be removed includegame information, such as scores, clock times, and other informationdisplayed on graphical information panels in the original content. Insome embodiments, the mapped virtual event data may then be incorporatedwith the desensitized video component of the virtual sporting event,such as by displaying within or overlaying on the video of the timesegments. In some embodiments the virtual event data may be displayed ona separate screen, window, or panel as the corresponding event segmentsare displayed.

At operation 410, a non-interactive audio component of the virtualsporting event is generated at audio creation module 260 based on thevideo component generated at operation 408. In some embodiments, thenon-interactive audio component includes the virtual audio mapped to theselected event segments. As such, the mapped virtual sounds may beplayed as the corresponding desensitized video component is displayed toa user, such as announcer 265.

Furthermore, the non-interactive audio component also includes narrativeaudio, which may be created by one or more human commentators, such asannouncer 265, viewing the video component. For example, the videocomponent of the virtual sporting event may be displayed at a userinterface for viewing by announcer 265 and/or other users. The recordednarrative audio may be combined with various virtual sounds, associatedwith virtual event 101 (e.g., crowd cheer, helmet hits) to generatenon-interactive audio content.

With reference to FIG. 5 , shown is an example user interface 500 fordisplaying a virtual sporting event, in accordance with one or moreembodiments. In various embodiments, user interface 500 iscommunicatively coupled to audio creation module 260. In someembodiments, user interface 500 may be an integrated component of audiocreation module 260. The video component may be displayed at display 504along with the virtual event data including the virtual sounds,fictional character information, and accumulated scoring and othervirtual game statistics. In some embodiments, the fictional characterinformation and virtual game statistics may be displayed on the userinterface at panel 506. In various embodiments, panel 506 may be agraphical information panel or overlay on the video component, oranother display separate from display 504. Virtual audio 472 may bepresented via audio output device 508. Announcer 265 may then record thenarrative audio into an audio input device 510 based on the videocomponent and virtual event data. Narrative audio may comprisecommentary, analysis, or other dialogue pertaining to the virtualsporting event and associated event data. As used herein, thenon-interactive audio component generated at audio creation module 260refers to an audio component comprising audio selected and createdentirely by virtual event system 200 and excluding audio from externalsources, such as client devices.

In some embodiments the narrative audio may also comprise advertisementsor other information from sponsors. In some embodiments, advertisementsand information from sponsors may be separate audio data that isincorporated into the audio component prior to or during broadcast ortransmission of the audio component to client devices corresponding tothe audience members. In some embodiments, the commentary may also beautomatically generated by a computer system which provides speech anddialogue based on the virtual event data. For example, a computingsystem may generate a script based on the mapped virtual event data andimplement text-to-speech programs to read the script.

Various interactive features may be added to non-interactive audiocomponent to enhance the overall audio experience and, morespecifically, to create a more realistic representation of a live event.Interactive audio module 280 may be configured to receive and aggregateuser input from the one or more client devices to create an interactiveaudio component. In some embodiments, users (279) receiving the virtualsporting event audio at client devices (278) may transmit user input tovirtual event system 200, including audio input, video input, andvarious other selections. In some embodiments, interactive audio may begenerated from received the audio input including various audio datacreated by listening users, such as user 279, which may includecheering, jeers, comments, clapping, etc. In some embodiments, audiocreation module 280 may generate audio data or retrieve virtual audiofrom virtual event database 252 based on the user input.

Interactive audio module 280 may be configured to combine theinteractive audio with the non-interactive audio for transmission to atleast a portion of the audience members as a hybrid audio component overthe network by delivery module 270. As such, virtual event system 200may implement a feedback loop, wherein users listening to the virtualsporting event create interactive audio, which is then transmitted tothe users in real-time.

At operation 412, the audio component is transmitted or stored. In someembodiments, the non-interactive audio may be transmitted via deliverymodule 270. In some embodiments, delivery module 270 may include adatabase for storing audio components for one or more virtual sportingevents. Delivery module 270 may be configured to transmit the audiocomponent (non-interactive or interactive) as an audio file to anetwork, such as network 110. In various embodiments, network 110 may bea global network such as the Internet. In some embodiments, the audiocomponent is transmitted to client devices, such as client device 278,over Wi-Fi or mobile data. In some embodiments, the audio component istransmitted via radio station server 272 for broadcast via radiotransmitter 274. The radio broadcast may then be received by radioreceiver 276 and output to user 279. In some embodiments radio receiver276 may be a component of user device 278.

Methods of Segment Selection

Virtual Coach/Team Selection

As previously described, event segments may be selected by one or moremethods, such as at operation 404. Such methods may comprise varioussegment selection algorithms. With reference to FIGS. 6A-6D, shown aremethods for selecting event segments for various time segments of avirtual sporting event, in accordance with one or more embodiments.

A Virtual Coach or Virtual Team method may be implemented to selectevent segments that include plays with characteristics orclassifications based on the predicted decisions of existing teams orcoaches and managers of existing teams. With reference to FIG. 6A, shownis an example method 600-A for selecting event segments using a VirtualCoach or Virtual Team method, in accordance with one or moreembodiments.

At step 602, statistical play data corresponding to historical sportingevents is processed. In some embodiments, the statistical play data maybe related to, derived from, or comprised of the statistical play datain the associated content. In some embodiments, statistical play datamay be received from third-party vendors. The steps of method 600-A willbe described mainly with reference to American football. However, itshould be recognized that various other methods of organizingstatistical play data may be implemented for different sporting events.In some embodiments, processing the statistical play data may compriseassigning tags to the statistical play data to associate particular datawith a grouping. In some embodiments, processing the statistical playdata may comprise creating groupings and counting the number of playsthat fall within each grouping.

During the processing, the information in the statistical play data maybe used to determine decision-making probabilities for each profile. Insome embodiments, the statistical play data may be organized byparticular play profiles corresponding to particular teams or coaches.For example, statistical play data relating to a team, such as theOakland Raiders, may be grouped or otherwise associated with an OaklandRaiders play profile. As another example, statistical play data relatingto play decisions made by a coach, such as Pete Carroll, may beassociated with a Pete Carroll play profile. In some embodiments, thestatistical play data may be also be grouped by particular years orseasons. For example, the statistical play data for the Oakland Raidersmay be grouped by seasons from 2010 to 2018.

In some embodiments, the plays for each down and yardage requirement atvarious field positions may be determined and counted. The statisticalplay data may be further organized by downs, yardage requirements, andfield positions. In some embodiments, all first down plays, second downplays, third down plays, and fourth down plays may be determined andgrouped based on yardage requirements. For example, all second downplays with 10 yards to the first-down line (2nd and 10) may be grouped.The system may similarly group all 2nd and 9 plays, 2nd and 8 plays, 2ndand 7 plays, and so on. In some embodiments, the plays may be grouped byranges of yardage requirements. For example, second down plays with 0-2yards to go may be grouped, second down plays with 2 or more to 6 yardsto go may be grouped, second down plays with 6 or more to 10 yards maybe grouped, and second down plays with 10 or more yards to go may begrouped. The ranges of yard requirements may be determined based on anumber of different features or characteristics.

The statistical play data may be further organized by ranges of fieldpositions. The ranges of field position may vary in size and may varybetween different profiles. For example, the relevant yard ranges forthe Oakland Raiders profile may be the 0 to 20 yard line up-field, the20 to 50 yard line up-field, the 50 to 30 yard line down-field, the 30to 10 yard line down-field, and the 10 yard line to the goal linedown-field. The statistical play data may be organized into additionalgroups or fewer groups than those described. Additional groups mayinclude ranges of in-game clock time and weather conditions.

Play statistics for each play profile may then be determined based onthe groupings. The proportion of various play types within each groupingmay be determined. For example, the percentage of occurrence of eachplay type that occurs at various down and yardage requirements atvarious field positions may be determined. For example, the OaklandRaiders profile may indicate that on when the team is within thedown-field 20 yard line to 10 yard line on third down with 6 or more to10 yards to go, the team passes the ball 90% of the time, runs the ball9% of the time and attempt a field goal 1% of the time.

At step 604, a play profile is assigned to at least one team of thevirtual sporting event. For example, the Oakland Raiders play profilemay be assigned to one of the teams of the virtual sporting event. Thus,when the assigned team is on offense, method 600-A will be used toselect event segments for the assigned team. In some embodiments, bothteams in the virtual sporting event may be assigned a play profile. Insome embodiments, more than one play profile may be assigned to aparticular team. If more than one play profile is assigned to aparticular team, the play statistics for groupings in both profiles maybe combined. As such, it is possible to create a game between twoexisting teams based on the statistically likely decisions that would bemade by such teams in a desired year or season.

At step 606, the current virtual statistical play data is received. Aspreviously described, the event data module may aggregate the virtualevent data in selected event segments to derive the current virtualstatistical play data, such as at operation 406. The current virtualstatistical play data may indicate that the assigned team is on offense.If the assigned team is currently on offense, then a set of valid eventsegments for the current time segment is received at step 608. The setof valid event segments may be identified at operation 402 as previouslydescribed.

At step 610, the probabilities of possible subsequent plays aredetermined based on the play statistics of the assigned play profile. Invarious embodiments, the segment selection module may reference thestatistical play data processed at step 602 to determine theprobabilities of possible subsequent plays based on the current virtualstatistical play data. For example, if the current virtual statisticalplay data indicates that the ball is at the 12 yard line during a thirddown, then the segment selection module references the percentages ofeach play type in the assigned profile in that situation (down, yardrequirement, and field position). In the present example, the assignedOakland Raiders profile would indicate a 90% chance for selecting a passplay, a 9% chance of selecting a run play, and a 1% chance of selectinga field goal attempt.

Method 600-A may then proceed to step 612 to determine a play type basedon the probabilities determined at step 610. In the present example, thesegment selection module may determine a play type from a pass play, runplay, or a field goal attempt. The likelihood that each type of playwill be determined may be based on the determined probabilities. Thus,there would be a 90% chance of selecting a pass play, a 9% chance ofselecting a run play, and a 1% chance of selecting a field goal attempt.

At step 614, method 600-A selects a valid event segment that includesidentified characteristics or classified categories that correspond tothe determined play type. In some embodiments, an event segmentcorresponding to such play type is randomly selected from the set ofvalid event segments. In some embodiments, a subset of valid eventsegments that correspond to the determined play type may be determinedand a single event segment is selected from the subset based onadditional factors or by other segment selection methods.

Alternatively, method 600-A may then proceed to step 616 to determine asubset of valid event segments that correspond with the play typesdetermined at step 610. The play types represented in the subset ofvalid event segments may be in proportion to the probabilitiesdetermined at step 610. The segment selection module may filter the setof valid event segments by identifying a filtered subset of valid eventsegments that include plays in proportion with the determinedprobabilities. In some embodiments, the segment selection module mayidentify a predetermined number of event segments. The predeterminednumber may be a maximum number. For example, the segment selectionmodule may identify 100 event segments to select for a particular timesegment. The 100 identified event segments will include a proportionalamount of event segments corresponding to the probabilities of playtypes determined at step 610. Thus, in the present example, 90 out ofthe 100 identified event segments will correspond to pass plays, 9 outof 100 identified event segments will correspond to run plays, and 1 outof 100 identified event segments will correspond to field goal attempts.

In various embodiments, the filtered subset of event segments may beidentified randomly. For example, the 90 event segments corresponding topass plays may be randomly identified from the segment database. A validevent segment is then selected from the filtered set of event segmentsat step 616. For example, the valid event segment may be randomlyselected from the filtered subset of valid event segments. In someembodiments, the selection may be further narrowed by other factors suchas success rate of the play indicated by the event segment, or byanother segment selection method.

In some embodiments, a Virtual Coach or Virtual Team algorithm mayimplement a neural network trained on data compiled for an existingcoach or team to select event segments based on decisions correspondingto a combination of one or more teams or coaches. For example, traininginput may include values corresponding to various characteristics at aparticular play in a historical sporting event, such as the clock time,the current score, the position on the field, and the down and yardagerequirements. The training input may be input as a feature vector alongwith a known training output value corresponding to the type of play,such as a pass, run, etc. Based on the training data, the neural networkmay update weighted coefficients for each characteristic used in thetraining data. Once fully trained, the probabilities of various types ofplays may be determined based on the weighted coefficients andcharacteristics indicated by the current statistical play data of thevirtual sporting event.

Principled Play Selection

A Principled Play method may be implemented to select event segmentsbased on a combination of one or more specific programmed objectives.With reference to FIG. 6B, shown is an example Principled Play method600-B for selection of event segments, in accordance with one or moreembodiments. In some embodiments, the Principled Play algorithm selectsevent segments based on the classifications or characteristics of theevent segments that support the programmed objectives.

At step 621, one or more programmed objectives are received, which mayinclude corresponding predetermined conditions. For example, suchprogrammed objectives may include selecting plays that generate the mostyardage gained. The programmed objectives may include selecting playswith the most crowd excitement based on audio characteristics of crowdsound or social media posts. The programmed objectives may includeselecting plays that result in the highest scores. The programmedobjectives may include selecting plays under a certain length of time.

The predetermined conditions associated with the programmed objectivesmay identify qualifying events for the programmed objectives. Forexample, the programmed objectives may be desired to occur at particulardowns in a virtual football game, such as to select plays that generatethe most excitement during third down plays. As another examples,programmed objectives may be desired to occur with a particular amountof in-game clock time remaining, such as to select plays under a certainlength of time when there are two minutes or less remaining in the clocktime.

For example, the programmed objective may be to select event segmentswith third-down plays that generate the most yardage. The algorithm maythen identify a group of all valid event segments corresponding tothird-down plays that resulted in a gain of twenty or more yards.

At step 623, current virtual statistical play data is received. Aspreviously described, the event data module may aggregate the virtualevent data in selected event segments to derive the current virtualstatistical play data, such as at operation 406. It is then determinedwhether a predetermined condition is present at 625. If thepredetermined condition is not present, method 600-B may return to step623 to continue receiving virtual statistical play data as eventsegments are selected via other segment selection methods. If thepredetermined condition is present, method 600-B proceeds to step 627 toselect a valid event segment that includes characteristics or categoriesthat match the criteria of the programmed objectives. In someembodiments, there may be no predetermined conditions and the programmedobjectives may apply to all time segments. As such, in some embodiments,step 625 may be an optional step.

At step 627, a valid event segment with characteristics or categoriesthat match the criteria of the one or more programmed objectives isselected. The characteristics or categories of each valid event segmentmay be identified and classified, respectively, during the segmentationprocess of method 300. In some embodiments, an event segment withmatching criteria may be randomly selected from the set of valid eventsegments. In some embodiments, additional programmed objectives may beapplied. The identified group of all valid event segments may benarrowed down by programmed objectives until a single event segment canbe identified. In some embodiments, the Principled Play algorithmimplements a predetermined hierarchy of programmed objectives. In someembodiments, an event segment may also be selected from the set of validevent segments based on implementation of other segment selection methoddescribed herein.

It is then determined whether there are additional time segments at 629.If there are additional time segments requiring event segments to beselected, then method 600-B may return to step 623 to receive additionalvirtual statistical play data for subsequent time segments. If all eventsegments have been selected for all time segments, then method 600-Bends.

Interactive Play Selection

An Interactive Play algorithm may select event segments based onexternal input, such as that received by users listening to the virtualsporting event. For example, users, such as user 279, may usecorresponding client devices, such as client device 278, to input voteselections on the type of play to be selected for a subsequent timesegment. With reference to FIG. 6C, shown is an example Interactive Playmethod 600-C for selection of event segments, in accordance with one ormore embodiments. Although method 600-C described with reference todesired play types, it should be recognized that method 600-C may beimplemented to select event segments correspond to a number of othertypes of desired characteristics, such as players involved in a play,penalty calls, player substitutions, etc.

At step 631, a request for user input is transmitted to one or moreclient devices. In some embodiments, client device 278 may be any one ofclient devices 102-108 described with reference to FIG. 1 . The requestmay comprise audio and/or visual prompts to the user. For example, therequest of user input may comprise text which may display at the clientdevices via an application or web browser to request user inputcorresponding to a selection for one or more play types or strategies,such as pass plays, run plays, etc. The request for user input maydisplay selection options on the client device. For example, the requestmay display a selection for a run play and a selection for a pass play.As such, the user input may be an indication of a manual selection. Therequest may also comprise accompanying audio or video instructions forthe user selection. As another example, the request may request audiouser input. For example, the request may prompt users to cheer or yellif a pass play is desired, or to boo or jeer if a particular action inthe virtual sporting event is desired.

The request may be transmitted only to client devices corresponding tousers that meet predetermined criteria. For example, requests may onlybe transmitted to users who have registered through an application. Asanother example, advertising data may be used to identify users eligibleto receive requests for user input, such as users who have recentlypurchased fuel at participating gas stations.

At step 633, user input is received at the system. The input may bereceived at anyone of various components of system 200 via network 110.Such input may be transmitted via the network to segment selectionmodule 240 or other client device interface. In some embodiments, suchinput may be transmitted via the network to biasing module 290, whichwill be further described below. Such user input may be a user selectionin an application executed on the client device. In some embodiments,input may be audio input received at the client device, such ascheering, or yelling confirmation for a particular play type. The inputmay also be received as a text message or voice call sent via the clientdevice. The manner of receiving user input may be chosen by useractivity while listening to the virtual sporting event. For example, auser may be listening while driving in an automobile. The applicationmay provide an option for receiving audio input, which may requirelimited physical or visual interaction with the client device. In otherembodiments, the application may request confirmation that the user is apassenger or not currently driving before input may be received.

At step 635, the desired play type is determined based on the receiveduser input. Audio input may be processed by voice or speech recognitiontechniques to determine whether the nature of the user input, such aswhether they comprise cheers or jeers. The desired play type may bedetermined based on various predetermined requirements or thresholds.

In some embodiments, the selection option corresponding to the majorityof user input may determine the desired play type. In some embodiments,a desired play type may be determined if user input exceeds apredetermined threshold. For example, if user input is received frommore than 50% of client devices executing applications for receiving thevirtual sporting event, then the play type corresponding to the userinput may be determined to proceed.

An event segment with characteristics or classifications matching thecriteria of the determined play type is selected at step 637. Forexample, the majority of user input may indicate a selection for a teamto pass on the next fourth down. As such, the Interactive Play algorithmmay identify valid event segments corresponding to pass plays. A passplay event segment may then be selected randomly or based on anothersegment selection algorithm as described herein.

Exhaustive Play Selection

An Exhaustive Play algorithm may be implemented to determine anexhaustive set of complete virtual sporting events based on all possiblecombinations of available event segments. With reference to FIG. 6D,shown is an example Exhaustive Play method 600-D for selection of eventsegments, in accordance with one or more embodiments.

At step 641, a set of one or more complete virtual sporting events isdetermined based on available event segments. For example, a first eventsegment corresponding to the first play of a virtual football game maybe selected for a first time segment (t). In some embodiments, one ofthe various segment selection methods described herein may beimplemented to select event segments for each time segment of thevirtual sporting events in the set. Once an event segment is selectedfor a time segment (t) by one or more of the segment selection methods,constraints for the subsequent time segment (t+1) may be identifiedbased on the selected event segment for time segment (t), at operation402. A separate virtual sporting event may be created for each possibleevent segment that may be selected for a subsequent time segment (t+1).As such, method 400 may repeat operations 402 and 404 for each generatedvirtual sporting event until event segments have been selected for eachtime segment of all possible virtual sporting events.

At step 643, the set of complete virtual sporting events is filteredbased on desired results. The exhaustive set of complete virtualsporting events may then be evaluated based on a combination of variousdesired results, such as outcome, excitement level, etc. For example, acomplete virtual sporting event may be selected from the exhaustive setbased on the result that the outcome of the game is determined by thefinal play. The set of all possible complete virtual sporting events maybe filtered to obtain a subset of complete virtual sporting events withoutcomes that are determined by the final play. Then a complete virtualsporting event may be selected from the filtered subset at step 645. Acomplete virtual sporting event may be selected at step 645 at random orbased on other desired results, such as victory by a particular team.

Manipulation of Virtual Event Outcomes

In some embodiments, input from client devices may also be used byvirtual event system 200 for controlling or changing the outcome of thevirtual sporting events. Such input, or external stimulus, may be usedto alter the selection of event segments used for one or more timepositions of the virtual event. The external stimulus may be appliedbefore the start of the virtual sporting event or during the virtualsporting event. With reference to FIG. 7A, shown is an example process700 for manipulating selection of event segments based on audienceinput, in accordance with one or more embodiments. FIG. 7B illustrates aprocess flowchart for manipulating event segment selection, inaccordance with one or more embodiments.

In certain embodiments, such biasing of selected event segments may beimplemented in conjunction with, or as part of, an Interactive Playmethod, such as method 600-C, for selection of event segments. However,in some embodiments, process 700 may be implemented with various othersegment selection mechanisms described herein. For example, audiencemembers may be directed to provide user input to increase the chances ofa successful play to be selected for a particular event segment. In someembodiments, if the amount of user input exceeds a predeterminedthreshold, the segment selection module may bias the segment selectionby increasing the ratio of successful plays to unsuccessful plays. Assuch, there would be a greater chance that a successful play isselected.

As another example, a coin flip at the start of a virtual football gamemay be affected by user input. Typically the odds of the coin landing onheads would be 50%. However, user input may affect the segment selectionmodule choice to selecting a resulting heads flip 60% of the time. Asanother example, when event segments are identified or selected, such asat operations 402 or 404, there may be several possible event segmentsthat may be selected. Biasing based on user input may be implemented toincrease the likelihood of choosing an event segment corresponding to asuccessful play. This may provide incentive for listening users toparticipate and provide user input to obtain higher odds for theirdesired outcome.

As shown in FIG. 7B, virtual sporting event 750 is generated by system200. Virtual sporting event 750 may comprise a plurality of timesegments, including at least time segment (t−1) to time segment (t+n).As shown, and as previously described, segment selection module 240 andbiasing module 290 are configured to select event segments as virtualevent segments 735 for each time segment of virtual sporting event 750.As virtual event segments 735 are selected, the event segments areretrieved from segment database 220 to generate virtual sporting event750 at audio creation module 260 and the corresponding audio componentis transmitted to user devices 278 via delivery module 270. FIGS. 7A and7B will be described with reference to selecting an event segment for acurrent time segment (t).

At operation 702, one or more event segments are identified as a set ofvalid event segments 731 for a given time segment of the virtualsporting event. The set of valid event segments 731 may be identifiedbased on event-specific constraints, such as at operation 402.Alternatively, the set of valid event segments 731 may be identifiedbased on one or more event selection algorithms, as described withreference to operation 404 (i.e., methods 600-A, 600-B, and 600-C). Insome embodiments, the set of valid event segments 731 may be identifiedby a combination of operations 402 and 404.

For example, a set of valid event segments may be first identified basedon event-specific constraints corresponding to the virtual event segmentselected for the previous time segment (t−1), as described with respectto operation 402. In some embodiments, the set of valid event segmentsmay be further refined by one or more of the segment selectionalgorithms described above to provide a subset of valid event segments.

As an illustrative example, virtual event data corresponding to thevirtual event segment selected for previous time segment (t−1) mayindicate that the ball is on the down-field 15 yard line at 3rd down. Assuch, segment selection module 240 identifies a set of valid eventsegments corresponding to plays starting at or near the 15 yard line.Alternately, or additionally, the set of valid event segments maycorrespond to 3rd down plays.

Next in the example, segment selection module 240 may implement aVirtual Team method to refine the set of valid event segments, such asvia the Oakland Raiders play profile. As in the example above, theOakland Raiders play profile indicates that when the team is within thedown-field 20 yard line to down-field 10 yard line on third down with 6to 10 yards to go, the team passes the ball 90% of the time, runs theball 9% of the time and attempt a field goal 1% of the time.

As such, segment selection module 240 may refine the set of valid eventsegments 731 to include 90% pass plays, 9% run plays and 1% field goalattempts, all of which start at or near the 15 yard line on 3rd down.Alternatively, segment selection module may select a play type based onthe probabilities of play types. For example, segment selection module240 may determine that a pass play will be selected for current timesegment (t). Thus, the set of valid event segments 731 may be refined toinclude only pass plays that start at or near the 15 yard line on 3rddown.

At operation 704, an outcome category for each of the valid eventsegments is determined. As previously described, each event segmentidentified in segment database 220 may be classified into one or morecategories. The outcome category of each valid event segment mayindicate an outcome of a play corresponding to the valid event segment.For example, plays resulting in a positive gain of yards may beclassified into successful play category, while plays resulting in anegative gain of yards may be classified into a failed play category.Such classifications may take into account yardage gained or lost due topenalties. Outcome categories may include further granularity byindicating amount of yards gained, which may further be grouped intoranges. Outcome categories may also include whether the event segmentcorresponds to a scoring play. The event segments may be classified intovarious other outcome categories based on corresponding statistical playdata.

At operation 706, user input 741 is obtained from client devices, suchas client device 278, associated with audience members. The user inputmay indicate a desired outcome for the virtual sporting event, or agiven time segment of the virtual sporting event. The user input maycorrespond to audience participation or audience feedback data. Similarto the external input received for the Interactive Play method forsegment selection, a request for user input may be transmitted to theone or more client devices. In some embodiments, requests may promptaudience members to input various forms of information.

For example, listeners may be required execute an application on thecorresponding client device in order to receive and listen to thetransmitted virtual sporting event 260. In some embodiments, listenersmay be prompted to register user profiles in the application, andprovide various types user information such as age, gender, location,and interests. A listener may also indicate which teams he or shesupports in the user profile. In some embodiments, credit cardinformation or other payment information may be saved for paymentrequirements, such as in-application purchases, subscriptions, etc.Other user information may include other registrations to relatedadvertising partners or ownership of products corresponding toadvertising partners.

Prompts for any combination of the abovementioned user information maybe transmitted to client devices via other means, such as via email,text message, etc. For example, permission to access locationinformation from the client device may be requested. Other user inputmay include audio input received from the client device, such as cheers,jeers, yelling, etc.

Other user input may include wagers placed to indicate support for agiven team, or placed on the outcome of the virtual sporting event or ofvarious plays or other events in the virtual sporting event, such as acoin flip at the beginning of a football game, or the result of the jumpball at the beginning of a basketball game. In some embodiments, wagersmay be placed as donations to charitable causes. For example, listenersmay agree to donate an amount of money to a charitable cause based onthe outcome of the virtual sporting event or particular play. As anotherexample, all submitted wagers may be aggregated and a particularcharitable cause may be selected based on the outcome of the virtualsporting event.

The audience feedback or audience participation data may indicate adesired outcome for the given time segment. For example, the desiredoutcome may correspond to a successful play, a failed play, a scoringplay, etc. The desired outcome may be indicated by any combination ofone or more of the user input received

In particular embodiments, the desired outcome may be indicated by thetotal number of current listeners that support a particular team. Thismay be based on which team has the majority of supporters currentlylistening to the virtual sporting event. For example, if Team A has morecurrent audience members registered as supporters than Team B at a giventime, then there may be an increased chance that the event segmentselected for time segment (t) will include a successful play for Team A.Alternatively, if Team A has the majority of supporting listeners, thenthere may be an increased chance that the selected event segment willinclude a failed play for Team B.

In some embodiments, the desired outcome may be indicated by the numberof current audience members listening from a particular location. Forexample, the commentary in the transmitted audio component may reportthat the number of audience members listening from a particular city orgeographic above a predetermined threshold will provide a successfuloutcome for a particular team.

In some embodiments, the listeners supporting a particular team thatgenerate the loudest cheering audio through the client devices willindicate a positive outcome for that particular team. As previouslydescribed, the client devices may be configured to collect audio inputfor receiving audio data from listeners. In some embodiments, the audiodata is received at biasing module 290. In some embodiments, the audiodata is received at interactive audio module 280. The audio datareceived at system 200 may be associated with the user profileindicating the team preference. In some examples, biasing module 290 isconfigured to determine the volume level of audio data received fromeach client device and calculate which team's supporters provided theloudest audio input. This may be a cumulative volume level of supportersfor each team.

In some embodiments, the desired outcome may be indicated by the numberof current listeners who post on social media. User input may correspondto social media posts relating to the virtual sporting event 750. Suchsocial media data maybe identified by hashtags or other forms of taggingand received at system 200. In some embodiments, the desired outcome maybe indicated by the amount of wagers submitted supporting a given teamor outcome.

In yet further embodiments, the desired outcome may be indicated by thenumber of current listeners with a connection to an advertising partner.For example, a particular automobile brand may sponsor a particularteam. Listeners may indicate the make of their vehicle through anapplication or other transmission from the corresponding client device.As such, the number of listeners that own cars with a make correspondingto the particular automobile brand may determine or affect a desiredoutcome for the team supported by the automobile brand.

At operation 708, a valid event segment 731 is selected as a virtualevent segment 735 for the current time segment (t) based on at least thedesired outcome. In some embodiments, biasing module 290 selects a validevent segment with an outcome category corresponding to the desiredoutcome determined at operation 706.

As an illustrative example, the desired outcome determined at operation706 is a scoring play for Team A. In the example above in which the setof valid event segments 731 includes only pass plays determined by theVirtual Team method, biasing module 290 may select a valid event segmentfrom set 731 corresponding to a pass play that results in a score forTeam A. The scoring pass play may be selected at random from the set ofvalid event segments, or based on other considerations described herein.

In some embodiments, the set of valid event segments 731 may be biasedto obtain a desired proportion of event segments with a particularoutcome category. This may result in a biased set of event segments 733with a desired proportion of event segments with outcome categoriescorresponding to the desired outcome.

As previously described, the set of valid event segments 731 may berefined to include only pass plays that start at or near the 15 yardline on 3rd down. Biasing module 290 may further refine the set of validevent segments such that the biased set of event segments 733 includesonly pass plays that result in a score. Virtual event segment 735 maythen be selected at random from the biased set 733. As another example,biasing module 290 may refine the set of valid event segments 731 suchthat the biased set 733 includes at 70% of event segments resulting in ascore. The remaining event segments may be a combination of failed playsand successful plays that do not result in a score.

The desired proportion may be determined by the nature of the user inputindicating audience participation. A larger quantity of user inputreceived from supporters of a particular team may increase the desiredproportion of successful plays in the valid event segments 731. Forexample, the initial proportion of successful plays may be 50%, or a 1:1ratio of successful plays to failed plays. If 10% more of the user inputis received by listeners supporting Team A, then the proportion ofsuccessful plays in set 731 may be increased by 10% to 60%.Alternatively, the proportion of successful plays may be increase bysome factor, such as 1.5 times the percentage majority, which would be15% in the example.

Alternatively, the set of valid event segments 731 may include eventsegments comprising of 90% pass plays, 9% run plays and 1% field goalattempts, all of which start at or near the 15 yard line on 3rd down.Biasing module 290 may refine the set of valid event segments 731 suchthat event segments corresponding to each type of play includes at leasta particular percentage categorized as a scoring play. Thus, theproportion of each type of play remains the same, while the percentageof scoring plays in each play type category is increased.

With reference to FIG. 8A, shown is an example method 800 formanipulating the outcome of a virtual sporting event based on audienceinput, in accordance with one or more embodiments. FIG. 8B illustrates aprocess flowchart for manipulating virtual sporting event outcomes, inaccordance with one or more embodiments. In some embodiments, method 800may be used to select a complete virtual sporting event (VSE) based onthe outcome of the complete virtual sporting event. As such, a set ofcomplete virtual sporting events is determined at operation 801. Forexample, system 200 may implement an Exhaustive Play method of segmentselection to determine a set of complete virtual sporting events 831, asdescribed with reference to FIG. 6D. Referring to FIG. 8B, segmentselection module 240 may implement method 600-D to determine a set ofcomplete virtual sporting events based on all possible combinations ofavailable event segments stored in segment database 220.

At operation 803, the outcome of each complete VSE is determined.Virtual event information may be mapped to each of the complete VSE inset 831. Virtual statistical play data for each complete virtualsporting event may be aggregated from the virtual event information todetermine the outcome of the complete VSE, such as which team is thewinning team. In some embodiments, the outcome of a particular event inthe complete VSE may be determined at operation 803, such as the outcomeof the coin flip at the beginning of a football game (i.e., head ortails), the kickoff team and receiving team for the kickoff at the firsthalf or the second half of a football game, the result of the kickoff(i.e., the kickoff return yardage).

At operation 805, user input 841 is obtained from client devices, suchas client device 278, associated with audience members. As describedwith reference to user input 741 at operation 706, user input 841 mayindicate a desired outcome for the VSE, or a given time segment of theVSE. In a first example, a desired outcome of a victory for Team A maybe determined by user input indicating a majority of wages were placedfor Team A. As a second example, the user input may indicate a desiredoutcome that Team A receives the kickoff at the beginning of a footballgame.

At operation 807, a complete VSE is selected as a chosen VSE 850 basedon at least the desired outcome. Based on the first example above,biasing module 290 may select a complete VSE from set 831 which resultsin a victory for Team A. Based on the second example above, biasingmodule 290 may select a complete VSE from set 831 which has Team Areceiving the kickoff at the beginning of the game. In some embodiments,this may occur by assigning Team A to the receiving team of an alreadyselected chosen VSE.

In some embodiments, the set of complete VSEs 831 may be biased toobtain a desired proportion of complete VSEs with a particular outcome.This may result in a biased set of VSEs 833 with a desired proportion ofVSEs with outcomes corresponding to the desired outcome. Based on thefirst example above, biasing module 290 may refine set 831 to obtain abiased set 833 which includes a predetermined proportion of VSEsresulting in a victory for Team A. The predetermined proportion may beany percentage, including up to 100%. The predetermined proportion maybe based on the quantity or nature of the user input received atoperation 805. Then chosen VSE 850 may be selected from the biased set833 at random, or based on other considerations.

Chosen VSE 850 may then be transmitted to audio creation module 260which may retrieve the appropriate event segments from segment database220 to generate video and audio components for chosen VSE 850. Audiocreation module 260 may then transmit the audio component to userdevices 278 via delivery module 270.

In some embodiments, selection of a complete VSEs based on user inputmay be implemented in real-time. In other words, after a particularchosen VSE 850 is selected, a different complete VSE may be selectedfrom set 831 or 833 based on user input during the transmission of eventsegments corresponding to the time segments.

Because the complete VSEs in set 831 comprise all possible combinationsof available event segments, there may be groupings of one or morecomplete VSEs with matching sequences of event segments up until aparticular time segment. As each time segment progresses to the next,the number of complete VSEs with matching sequences will decrease. Whena chosen VSE 850 is selected to be transmitted to audio creation module260, segment selection module 240 or biasing module 290 may track thegroup 837 of VSEs that match chosen VSE 850. As video components oraudio components of chosen VSE 850 are created, such as for time segment(t−1), at audio creation module 260, biasing module 290 is updated toremove VSEs from group 837 that branch away from the sequence of thechosen VSE 850 at time segment (t−1).

A desired outcome may then be determined at operation 805 for a timesegment occurring after segment (t−1). If chosen VSE 850 includes thedesired outcome, then no change may be made. However, if chosen VSE 850does not include the desired outcome, then biasing module 290 may selecta different VSE from group 837 as chosen VSE 850. As previouslydescribed, biasing module may select a different VSE with theappropriate outcome, or may bias group 837 to include a desiredproportion of matching VSEs with the appropriate outcome.

For example, while an audio component for a chosen VSE 850 is beingtransmitted to client devices 278, received user input may indicate adesired outcome of Team A completing a successful kickoff return at thestart of the second half. Biasing module 290 may then determine whetherthe current chosen BSE 850 results in this desired outcome. If it does,then no change will be made to the currently chosen VSE 850. However, ifthe currently chosen VSE 850 does not include the desired outcome,biasing module 290 may select a matching VSE from group 837 that doesinclude the desired outcome as a new chosen VSE 850.

Example Systems

Various computing devices can implement the methods described herein.For instance, a mobile device, computer system, etc. can be used togenerate dynamic ETA predictive updates. With reference to FIG. 9 ,shown is a particular example of a computer system 900 that can be usedto implement particular examples of the present disclosure. According toparticular example embodiments, a system 900 suitable for implementingparticular embodiments of the present disclosure includes a processor901, a memory 903, a transceiver 909, an interface 911, and a bus 915(e.g., a PCI bus). When acting under the control of appropriate softwareor firmware, the processor 901 is responsible for processing inputsthrough various computational layers and algorithms in a neural network.In some embodiments, the processor is responsible for updating theparameters of each computational layer using algorithms, including butnot limited to, a stochastic gradient descent algorithm and a backpropagation algorithm. Various specially configured devices can also beused in place of a processor 901 or in addition to processor 901. Thecomplete implementation can also be done in custom hardware.

The interface 911 is typically configured to send and receive datapackets or data segments over a network. Particular examples ofinterfaces the device supports include Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like. The interface 911 may include separate input and outputinterfaces, or may be a unified interface supporting both operations. Inaddition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

Transceiver 909 is typically a combination transmitter/receiver device.However system 900 may include a transmitter and a receiver as separatecomponents in some embodiments. Transceiver 909 may be configured totransmit and/or receive various wireless signals, including Wi-Fi,Bluetooth, etc. In some embodiments, system 900 may function as a clientdevice or location sensor or beacon to track location of an individualvia wireless signals. The connection or communication between a clientdevice and a beacon may indicate the presence of the correspondingindividual in a particular location. In various embodiments, transceiver909 may operate in a half duplex or full duplex mode. Various protocolscould be used including various flavors of Bluetooth, Wi-Fi, light ofsight transmission mechanisms, passive and active RFID signals, cellulardata, mobile-satellite communications, as well as LPWAN, GPS, and othernetworking protocols. According to various embodiments, the transceivermay operate as a Bluetooth or Wi-Fi booster or repeater.

According to particular example embodiments, the system 900 uses memory903 to store data and program instructions for operations includingprocessing video and audio data or files. Such operations may alsoinclude training a neural network, or selecting event segments, such asdescribed in method 400 and methods 600-A, 600-B, 600-C, and/or 600-D.The program instructions may control the operation of an operatingsystem and/or one or more applications, for example. The memory ormemories may also be configured to store received metadata and batchrequested metadata. The memory or memories may also be configured tostore data corresponding to parameters and weighted factors.

In some embodiments, system 900 further comprises a graphics processingunit (GPU) 905. The GPU 905 may be implemented to process video andimages of original content data, such as at operations 304, 306, 310,312, 314, and 408 among others. In some embodiments, system 900 furthercomprises an accelerator 907. In various embodiments, accelerator 907 isa rendering accelerator chip, which may be separate from the graphicsprocessing unit. Accelerator 907 may be configured to speed up theprocessing for the overall system 900 by processing pixels in parallelto prevent overloading of the system 900. For example, in certaininstances, ultra-high-definition images may be processed, which includemany pixels, such as DCI 4K or UHD-1 resolution. In such instances,excess pixels may be more than can be processed on a standard GPUprocessor, such as GPU 905. In some embodiments, accelerator 907 mayonly be utilized when high system loads are anticipated or detected.

In some embodiments, accelerator 907 may be a hardware accelerator in aseparate unit from the CPU, such as processor 901. Accelerator 907 mayenable automatic parallelization capabilities in order to utilizemultiple processors simultaneously in a shared memory multiprocessormachine. The core of accelerator 907 architecture may be a hybrid designemploying fixed-function units where the operations are very welldefined and programmable units where flexibility is needed. In variousembodiments, accelerator 907 may be configured to accommodate higherperformance and extensions in APIs, particularly OpenGL 2 and DX9.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

CONCLUSION

Although many of the components and processes are described above in thesingular for convenience, it will be appreciated by one of skill in theart that multiple components and repeated processes can also be used topractice the techniques of the present disclosure.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the disclosure. It is therefore intended that the disclosure beinterpreted to include all variations and equivalents that fall withinthe true spirit and scope of the present disclosure.

What is claimed is:
 1. A method comprising: identifying one or moreevent segments as a set of valid event segments for a given time segmentof a virtual sporting event; determining an outcome category for eachvalid event segment of the set of valid event segments, wherein theoutcome category for each valid event segment indicates an outcome of aplay corresponding to the valid event segment; obtaining audience input,over a network, from a plurality of client devices associated with aplurality of audience members, wherein the audience input indicates adesired outcome for the given time segment; and selecting a valid eventsegment as a virtual event segment for the given time segment based onat least the desired outcome and the outcome category for each validevent segment.
 2. The method of claim 1, wherein selecting a valid eventsegment as a virtual event segment comprises: biasing the set of validevent segments to obtain a biased set of valid event segments, whereinthe biased set of valid event segments comprises a predeterminedproportion of valid event segments with outcome categories correspondingto the desired outcome; and selecting the virtual event segment from thebiased set of valid event segments.
 3. The method of claim 2, whereineach event segment of the one or more event segments comprises portionsof reference content data, the reference content data comprisingreference video, reference audio, and reference statistics of aplurality of original sporting events, wherein each event segment of theone or more event segments comprises corresponding video data segmentsfrom the reference video, audio data segments from the reference audio,and statistical play data from the reference statistics, and whereineach event segment of the one or more event segments are classified withone or more categories based on the portion of reference content datacorresponding to the event segments, wherein the one or more categoriesincludes the outcome category.
 4. The method of claim 3, furthercomprising: retrieving an event segment as a virtual event segment foreach time segment of the virtual sporting event including retrieving thebiased virtual event segment for the given time segment; desensitizingeach of the virtual event segments by at least partially removing colorand sound from each of the virtual event segments; mapping virtual eventdata to each of the virtual event segments, wherein the virtual eventdata comprises one or more selected from the group consisting of:virtual event audio and fictional character information; and generatingvirtual event information for the virtual sporting event based on themapped virtual event data and a progress of the virtual sporting eventindicated by the virtual event segments.
 5. The method of claim 4,wherein identifying the one or more event segments as the set of validevent segments comprises: receiving historical play data correspondingone or more historical sporting events; determining probabilities ofoccurrence for different play types at different progress points basedon the historical play data; for the given time segment, determining acurrent progress point based on virtual event information for virtualevent segments selected for one or more previous time segments; andidentifying the one or more valid event segments based on theprobabilities of occurrence such that the set of valid event segmentsincludes a proportion of valid event segments with play typescorresponding to the probabilities of occurrence for different playtypes.
 6. The method of claim 4, wherein identifying the one or moreevent segments as the set of valid event segments comprises: receiving aprogrammed objective, wherein the programmed objective includes one ormore predetermined conditions; determining current virtual eventinformation based on virtual event segments selected for previous timesegments; for the given time segment, determining if the predeterminedcondition is satisfied based on the current virtual event information;and if the predetermined condition is satisfied, identifying the one ormore valid event segments based the programmed objective such that eachvalid event segment includes a category that satisfies the programmedobjective.
 7. The method of claim 4, further comprising: displaying thedesensitized virtual event segments in conjunction with associatedvirtual event data and virtual event information; obtaining narrativeaudio corresponding to the desensitized virtual event segments andcorresponding virtual event data; creating a virtual event audio file,the virtual event audio file including non-interactive audio includingthe narrative audio and corresponding virtual event audio; andtransmitting the virtual event audio file, via the network, to theplurality of client devices associated with the plurality of audiencemembers.
 8. The method of claim 7, wherein identifying the one or moreevent segments as the set of valid event segments comprises: for thegiven time segment, obtaining audience feedback data from the pluralityof client devices associated with the plurality of audience members,wherein the audience feedback data indicates a desired category; andidentifying one or more valid event segments that include a categorycorresponding to the desired category.
 9. A method comprising:generating a set of complete virtual sporting events, wherein eachcomplete sporting event comprises a possible combination of eventsegments, each event segment corresponding to a time segment of therespective complete virtual sporting event; determining an outcome ofeach complete virtual sporting event in the set of complete virtualsporting events; obtaining audience input, over a network, from aplurality of client devices associated with a plurality of audiencemembers, wherein the audience input indicates a desired outcome; andselecting a complete virtual sporting event as a chosen virtual sportingevent based on at least the desired outcome and the outcome of eachcomplete virtual sporting event.
 10. The method of claim 9, whereinselecting a complete virtual sporting event as the chosen virtualsporting event comprises: biasing the set of complete virtual sportingevents to obtain a biased set of complete virtual sporting events,wherein the biased set comprises a predetermined proportion of completevirtual sporting events with outcomes corresponding to the desiredoutcome; and selecting the chosen virtual sporting event from the biasedset of complete virtual sporting events.
 11. The method of claim 9,further comprising: retrieving the event segments corresponding to thechosen virtual sporting event as virtual event segments; desensitizingeach of the virtual event segments by at least partially removing colorand sound from each of the virtual event segments; mapping virtual eventdata to each of the virtual event segments, wherein the virtual eventdata comprises one or more selected from the group consisting of:virtual event audio and fictional character information; and generatingvirtual event information for the chosen virtual sporting event based onthe mapped virtual event data and a progress of the chosen virtualsporting event indicated by the virtual event segments.
 12. A systemcomprising: one or more processors, memory, and one or more programsstored in the memory, the one or more programs comprising instructionsfor: identifying one or more event segments as a set of valid eventsegments for a given time segment of a virtual sporting event;determining an outcome category for each valid event segment of the setof valid event segments, wherein the outcome category for each validevent segment indicates an outcome of a play corresponding to the validevent segment; obtaining audience input, over a network, from aplurality of client devices associated with a plurality of audiencemembers, wherein the audience input indicates a desired outcome for thegiven time segment; and selecting a valid event segment as a virtualevent segment for the given time segment based on at least the desiredoutcome and the outcome category for each valid event segment.
 13. Theprogrammable device of claim 12, wherein selecting a valid event segmentas a virtual event segment comprises: biasing the set of valid eventsegments to obtain a biased set of valid event segments, wherein thebiased set of valid event segments comprises a predetermined proportionof valid event segments with outcome categories corresponding to thedesired outcome; and selecting the virtual event segment from the biasedset of valid event segments.
 14. The programmable device of claim 13,wherein each event segment of the one or more event segments comprisesportions of reference content data, the reference content datacomprising reference video, reference audio, and reference statistics ofa plurality of original sporting events, wherein each event segment ofthe one or more event segments comprises corresponding video datasegments from the reference video, audio data segments from thereference audio, and statistical play data from the referencestatistics, and wherein each event segment of the one or more eventsegments are classified with one or more categories based on the portionof reference content data corresponding to the event segments, whereinthe one or more categories includes the outcome category.
 15. Theprogrammable device of claim 14, further comprising: retrieving an eventsegment as a virtual event segment for each time segment of the virtualsporting event including retrieving the biased virtual event segment forthe given time segment; desensitizing each of the virtual event segmentsby at least partially removing color and sound from each of the virtualevent segments; mapping virtual event data to each of the virtual eventsegments, wherein the virtual event data comprises one or more selectedfrom the group consisting of: virtual event audio and fictionalcharacter information; and generating virtual event information for thevirtual sporting event based on the mapped virtual event data and aprogress of the virtual sporting event indicated by the virtual eventsegments.
 16. The programmable device of claim 15, wherein identifyingthe one or more event segments as the set of valid event segmentscomprises: receiving historical play data corresponding one or morehistorical sporting events; determining probabilities of occurrence fordifferent play types at different progress points based on thehistorical play data; for the given time segment, determining a currentprogress point based on virtual event information for virtual eventsegments selected for one or more previous time segments; andidentifying the one or more valid event segments based on theprobabilities of occurrence such that the set of valid event segmentsincludes a proportion of valid event segments with play typescorresponding to the probabilities of occurrence for different playtypes.
 17. The programmable device of claim 15, wherein identifying theone or more event segments as the set of valid event segments comprises:receiving a programmed objective, wherein the programmed objectiveincludes one or more predetermined conditions; determining currentvirtual event information based on virtual event segments selected forprevious time segments; for the given time segment, determining if thepredetermined condition is satisfied based on the current virtual eventinformation; and if the predetermined condition is satisfied,identifying one or more valid event segments based the programmedobjective such that each valid event segment includes a category thatsatisfies the programmed objective.
 18. The programmable device of claim15, further comprising: displaying the desensitized virtual eventsegments in conjunction with associated virtual event data and virtualevent information; obtaining narrative audio corresponding to thedesensitized virtual event segments and corresponding virtual eventdata; creating a virtual event audio file, the virtual event audio fileincluding non-interactive audio including the narrative audio andcorresponding virtual event audio; and transmitting the virtual eventaudio file, via the network, to the plurality of client devicesassociated with the plurality of audience members.
 19. The programmabledevice of claim 18, wherein identifying the one or more event segmentsas the set of valid event segments comprises: for the given timesegment, obtaining audience feedback data from the plurality of clientdevices associated with the plurality of audience members, wherein theaudience feedback data indicates a desired category; and identifying oneor more valid event segments that include a category corresponding tothe desired category.